Dental Anomalies in Saudi Arabia: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Epidemiological studies have shown varying prevalence rates of dental anomalies worldwide, ranging from 5.2% to 56.9%, with a higher rate of 90.4% in patients with cleft lip and palate. In Saudi Arabia, studies have also reported varied prevalence rates, likely due to genetic differences or sampling variations. However, no research has yet evaluated the quality of these studies or provided an overall prevalence estimate, which is the aim of the present study. This systematic review aims to assess the prevalence and types of dental anomalies across various regions of the Kingdom of Saudi Arabia (KSA). METHODS: A comprehensive literature search identified 10 relevant studies on different dental anomalies in Saudi Arabia. The quality of the enrolled studies was assessed using the Newcastle-Ottawa Scale (NOS), showing variability in the methodological quality of the included cohort studies, with several studies demonstrating a moderate to high risk of bias. RESULTS: Common anomalies included hypodontia, hyperdontia, microdontia, and impacted teeth. This study highlights the varying prevalence of dental anomalies in different regions of Saudi Arabia, ranging from 2.6% to 45.1%. CONCLUSIONS: This review highlights the need for early diagnosis and tailored treatment approaches to mitigate the clinical challenges posed by these anomalies, underscoring the importance of standardized diagnostic criteria and further research to understand regional and demographic differences in the prevalence of dental anomalies in Saudi Arabia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".